README.md

---
license: apache-2.0
task_categories:
- other
tags:
- poi-recommendation
- trajectory-prediction
- human-mobility
dataset_info:
- config_name: default
  features:
  - name: user_id
    dtype: string
  - name: trail_id
    dtype: string
  - name: inputs
    dtype: string
  - name: targets
    dtype: string
  splits:
  - name: train
    num_bytes: 1636839
    num_examples: 3836
  - name: validation
    num_bytes: 231399
    num_examples: 549
  - name: test
    num_bytes: 469775
    num_examples: 1097
  download_size: 590806
  dataset_size: 2338013
- config_name: tabular
  features:
  - name: trail_id
    dtype: string
  - name: user_id
    dtype: int64
  - name: venue_id
    dtype: int64
  - name: latitude
    dtype: float64
  - name: longitude
    dtype: float64
  - name: name
    dtype: string
  - name: address
    dtype: string
  - name: venue_category
    dtype: string
  - name: venue_category_id
    dtype: string
  - name: venue_category_id_code
    dtype: int64
  - name: venue_city
    dtype: string
  - name: venue_city_latitude
    dtype: float64
  - name: venue_city_longitude
    dtype: float64
  - name: venue_country
    dtype: string
  - name: timestamp
    dtype: string
  splits:
  - name: train
    num_bytes: 2005420
    num_examples: 9691
  - name: validation
    num_bytes: 281184
    num_examples: 1357
  - name: test
    num_bytes: 577126
    num_examples: 2791
  download_size: 762968
  dataset_size: 2863730
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
- config_name: tabular
  data_files:
  - split: train
    path: tabular/train-*
  - split: validation
    path: tabular/validation-*
  - split: test
    path: tabular/test-*
---

# Massive-STEPS-Tokyo

<div align="center">

[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97-Hugging_Face_Collections-yellow)](https://huggingface.co/collections/CRUISEResearchGroup/massive-steps-point-of-interest-check-in-dataset-682716f625d74c2569bc7a73)
[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97-Hugging_Face_Papers-yellow)](https://huggingface.co/papers/2505.11239)
[![arXiv](https://img.shields.io/badge/arXiv-2505.11239-b31b1b.svg)](https://arxiv.org/abs/2505.11239)
[![GitHub](https://img.shields.io/badge/github-%23121011.svg?logo=github&logoColor=white)](https://github.com/cruiseresearchgroup/Massive-STEPS)

</div>

## Dataset Summary

**[Massive-STEPS](https://github.com/cruiseresearchgroup/Massive-STEPS)** is a large-scale dataset of semantic trajectories intended for understanding POI check-ins. The dataset is derived from the [Semantic Trails Dataset](https://github.com/D2KLab/semantic-trails) and [Foursquare Open Source Places](https://huggingface.co/datasets/foursquare/fsq-os-places), and includes check-in data from 15 cities across 10 countries. The dataset is designed to facilitate research in various domains, including trajectory prediction, POI recommendation, and urban modeling. Massive-STEPS emphasizes the importance of geographical diversity, scale, semantic richness, and reproducibility in trajectory datasets.

| **City**        |                                 **URL**                                 |
| --------------- | :---------------------------------------------------------------------: |
| Bandung 🇮🇩       |    [🤗](https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Bandung/)    |
| Beijing 🇨🇳       |    [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Beijing/)    |
| Istanbul 🇹🇷      |   [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Istanbul/)    |
| Jakarta 🇮🇩       |    [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Jakarta/)    |
| Kuwait City 🇰🇼   |  [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Kuwait-City/)  |
| Melbourne 🇦🇺     |   [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Melbourne/)   |
| Moscow 🇷🇺        |    [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Moscow/)     |
| New York 🇺🇸      |   [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-New-York/)    |
| Palembang 🇮🇩    |    [🤗](https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Palembang/)   |
| Petaling Jaya 🇲🇾 | [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Petaling-Jaya/) |
| São Paulo 🇧🇷     |   [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Sao-Paulo/)   |
| Shanghai 🇨🇳      |   [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Shanghai/)    |
| Sydney 🇦🇺        |    [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Sydney/)     |
| Tangerang 🇮🇩    |    [🤗](https://huggingface.co/datasets/CRUISEResearchGroup/Massive-STEPS-Tangerang/)   |
| Tokyo 🇯🇵         |     [🤗](https://huggingface.co/datasets/cruiseresearchgroup/Massive-STEPS-Tokyo/)     |

### Dataset Sources

The dataset is derived from two sources:

1. **Semantic Trails Dataset**: 
   - Repository: [D2KLab/semantic-trails](https://github.com/D2KLab/semantic-trails)
   - Paper: Monti, D., Palumbo, E., Rizzo, G., Troncy, R., Ehrhart, T., & Morisio, M. (2018). Semantic trails of city explorations: How do we live a city. *arXiv preprint [arXiv:1812.04367](https://arxiv.org/abs/1812.04367)*.
2. **Foursquare Open Source Places**: 
    - Repository: [foursquare/fsq-os-places](https://huggingface.co/datasets/foursquare/fsq-os-places)
    - Documentation: [Foursquare Open Source Places](https://docs.foursquare.com/data-products/docs/access-fsq-os-places)

## Dataset Structure

```shell
.
├── tokyo_checkins_test.csv # test set check-ins
├── tokyo_checkins_train.csv # train set check-ins
├── tokyo_checkins_validation.csv # validation set check-ins
├── tokyo_checkins.csv # all check-ins
├── data # trajectory prompts
│   ├── test-00000-of-00001.parquet
│   ├── train-00000-of-00001.parquet
│   └── validation-00000-of-00001.parquet
└── README.md
```

### Data Instances

An example of entries in `tokyo_checkins.csv`:

```csv
trail_id,user_id,venue_id,latitude,longitude,name,address,venue_category,venue_category_id,venue_category_id_code,venue_city,venue_city_latitude,venue_city_longitude,venue_country,timestamp
2018_18009,901,1425,35.59492076661418,139.34504702908666,JR 橋本駅 (JR Hashimoto Sta.),緑区橋本6-1-25,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-03 14:55:00
2018_18009,901,191,35.595137562150846,139.34373266637422,京王 橋本駅 (KO45),緑区橋本2-3-2,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-03 14:59:00
2018_18010,901,38,35.64444469921653,139.35434304296533,北野駅 (Kitano Sta.) (KO33),打越町335-1,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-04 11:57:00
2018_18010,901,3,35.65808032639735,139.34275103595496,京王八王子駅 (Keiō-hachiōji Sta.),明神町3-27-1,Train Station,4bf58dd8d48988d129951735,59,Hachiōji,35.65583,139.32389,JP,2017-10-04 12:00:00
2018_18010,901,2684,35.65829873991196,139.34315085411072,ローソン 京王八王子駅前店,明神町4-6-13,Convenience Store,4d954b0ea243a5684a65b473,215,Hachiōji,35.65583,139.32389,JP,2017-10-04 12:04:00
```

### Data Fields

| **Field**                | **Description**                    |
| ------------------------ | ---------------------------------- |
| `trail_id`               | Numeric identifier of trail        |
| `user_id`                | Numeric identifier of user         |
| `venue_id`               | Numeric identifier of POI venue    |
| `latitude`               | Latitude of POI venue              |
| `longitude`              | Longitude of POI venue             |
| `name`                   | POI/business name                  |
| `address`                | Street address of POI venue        |
| `venue_category`         | POI category name                  |
| `venue_category_id`      | Foursquare Category ID             |
| `venue_category_id_code` | Numeric identifier of category     |
| `venue_city`             | Administrative region name         |
| `venue_city_latitude`    | Latitude of administrative region  |
| `venue_city_longitude`   | Longitude of administrative region |
| `venue_country`          | Country code                       |
| `timestamp`              | Check-in timestamp                 |

### Dataset Statistics

| City        | Users  | Trails  | POIs  | Check-ins | #train | #val  | #test |
| ----------- | :----: | :-----: | :---: | :-------: | :----: | :---: | :---: |
| Tokyo 🇯🇵 | 764 | 5,482 | 4,725 | 13,839 | 3,836 | 549  | 1,097 |

## Additional Information

### License

```
Copyright 2024 Foursquare Labs, Inc. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
```

## 🔖 Citation

If you find this repository useful for your research, please consider citing our paper:

```bibtex
@misc{wongso2025massivestepsmassivesemantictrajectories,
  title         = {Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks},
  author        = {Wilson Wongso and Hao Xue and Flora D. Salim},
  year          = {2025},
  eprint        = {2505.11239},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2505.11239}
}
```

### Contact

If you have any questions or suggestions, feel free to contact Wilson at `w.wongso(at)unsw(dot)edu(dot)au`.